Dynamic Parameters

primacy(n_item, L, P1, P2)

Primacy gradient in learning rate.

p_stop_op(n_item, X1, X2[, pmin])

Probability of stopping based on output position.

prepare_list_param(n_item, n_sub, param, …)

Prepare parameters that vary within list.

Model Initialization

init_loc_cmr(n_item, param)

Initialize localist CMR for one list.

init_network(param_def, patterns, param, …)

Initialize a network with pattern weights.

study_list(param_def, param, item_index, …)

Simulate study of a list.

Model Framework


Context Maintenance and Retrieval-Distributed model.

CMRDistributed.fit_indiv(data, param_def[, …])

Fit parameters to individual subjects.

CMRDistributed.generate(data, group_param[, …])

Generate simulated data for all subjects.

CMRDistributed.record_network(data, param[, …])

CMRDistributed.parameter_sweep(data, …[, …])

Simulate data with varying parameters.

CMRDistributed.parameter_recovery(data, …)

Run multiple iterations of parameter recovery.